EP1204076A2 - Verfahren und Vorrichtung zum Überwachen des Betriebes einer Gasturbine - Google Patents

Verfahren und Vorrichtung zum Überwachen des Betriebes einer Gasturbine Download PDF

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Publication number
EP1204076A2
EP1204076A2 EP01307221A EP01307221A EP1204076A2 EP 1204076 A2 EP1204076 A2 EP 1204076A2 EP 01307221 A EP01307221 A EP 01307221A EP 01307221 A EP01307221 A EP 01307221A EP 1204076 A2 EP1204076 A2 EP 1204076A2
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EP
European Patent Office
Prior art keywords
engine
parameters
model
trend
trend parameters
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP01307221A
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English (en)
French (fr)
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EP1204076A3 (de
Inventor
Sridhar Adibhatla
Malcolm John Ashby
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General Electric Co
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General Electric Co
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Filing date
Publication date
Application filed by General Electric Co filed Critical General Electric Co
Publication of EP1204076A2 publication Critical patent/EP1204076A2/de
Publication of EP1204076A3 publication Critical patent/EP1204076A3/de
Withdrawn legal-status Critical Current

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Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02C—GAS-TURBINE PLANTS; AIR INTAKES FOR JET-PROPULSION PLANTS; CONTROLLING FUEL SUPPLY IN AIR-BREATHING JET-PROPULSION PLANTS
    • F02C9/00—Controlling gas-turbine plants; Controlling fuel supply in air- breathing jet-propulsion plants
    • F02C9/26—Control of fuel supply
    • F02C9/28—Regulating systems responsive to plant or ambient parameters, e.g. temperature, pressure, rotor speed
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02C—GAS-TURBINE PLANTS; AIR INTAKES FOR JET-PROPULSION PLANTS; CONTROLLING FUEL SUPPLY IN AIR-BREATHING JET-PROPULSION PLANTS
    • F02C9/00—Controlling gas-turbine plants; Controlling fuel supply in air- breathing jet-propulsion plants
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • G05B23/0254—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
    • F05D2260/00—Function
    • F05D2260/80—Diagnostics
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
    • F05D2260/00—Function
    • F05D2260/81—Modelling or simulation
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
    • F05D2260/00—Function
    • F05D2260/82—Forecasts

Definitions

  • This application relates generally to gas turbine engines and, more particularly, to methods and apparatus for trending gas turbine engine operation.
  • the engines may become less efficient due to a combination of factors including wear and damage. Because the rate at which engines deteriorate depends on several operational factors, the rate is difficult to predict, and as such, engine components are typically scheduled for maintenance based on a pre-selected number of hours or cycles. The pre-selected number is typically conservatively selected based on a number of factors including past component experience and past engine health estimates. If a component fails, a predetermined diagnosis routine is followed to identify and replace the failed component.
  • engine parameters are sensed and monitored to estimate an overall loss in engine performance.
  • rotor speeds, exhaust gas temperatures, and fuel flows are corrected or normalized for variations in operating conditions, and these normalized parameters are trended, i.e., their changes over short and long periods of time are plotted, and used to forecast when engine refurbishment is required. Additionally, immediate engine repairs may be scheduled if comparing current trending values to prior trending values illustrates abrupt changes, or step changes.
  • engine models and parameter estimation algorithms are used to track engine health and provide "health estimates" of engine components.
  • Known trending estimation algorithms account for variations in operating conditions, but do not account for engine quality and engine deterioration effects. More specifically, because of the complexity of the computations, known correction factors and parameter estimation algorithms do not provide reliable estimations and trend parameters during real-time engine operation.
  • a model-based trending process for a gas turbine engine generates, in real-time, engine trend parameters from engine sensor data and ambient flight condition data to assess engine condition.
  • the engine includes a plurality of sensors that are responsive to engine operations.
  • the trending process is implemented using a commercially available processor coupled to the engine to monitor the engine operations, and having the desired processing speed and capacity.
  • the trending process estimates engine health parameters for use in a model for component diagnostics and fault detection and isolation.
  • the interactions and physical relationships of trend parameters within the engine cycle are retained to permit substantially all sensed and model-generated virtual parameters for trending to be generated simultaneously.
  • the trending process accounts for engine quality and deterioration effects and provides engine health estimates that facilitate improving estimates of performance parameters or "virtual sensors" for use in trending engine operation.
  • Figure 1 is a flow chart illustrating an exemplary embodiment of a model-based normalization process 10.
  • Figure 2 is a schematic diagram of an engine model 12 that may be used to estimate sensed parameters with a model-based normalization process, such as process 10 shown in Figure 1.
  • engine 14 is a commercial engine such as a CFM56, CF6, or GE90 engine commercially available from General Electric Company, Cincinnati, Ohio.
  • engine 14 is an industrial aeroderivative engine such as the LM6000 engine commercially available from General Electric Company, Cincinnati, Ohio.
  • engine 14 is a military engine such as the F110 or F414 engine commercially available from General Electric Company, Cincinnati, Ohio.
  • System 10 could be implemented using, for example, a commercially available processor (not shown) having the desired processing speed and capacity.
  • System 10 includes a memory coupled to the processor, and is coupled to engine 14 to monitor engine operations.
  • Engine 14 includes a plurality of sensors (not shown) which monitor engine operation and input 20 real-time actual engine sensor data during engine operation to engine model 12.
  • the sensors monitor engine rotor speeds, engine temperatures, and engine pressures.
  • Ambient flight condition data is also input 24 to engine model 12.
  • ambient flight condition data input 24 includes, but is not limited to, ambient temperature, ambient pressure, aircraft mach number, and engine power setting parameters such as fan speed or engine pressure ratio. Collecting ambient flight condition data and actual engine sensor data is known in the art.
  • Engine model 12 is used to estimate sensed parameters, such as rotor speeds, temperatures, and pressures, as well as computed parameters such as thrust, airflows, stall margins, and turbine inlet temperature, based on environmental conditions, power setting parameters, and actuator positions input into engine model 12.
  • engine model 12 is a physics-based aero-thermodynamic model 26.
  • engine model 12 is a regression-fit model.
  • engine model 12 is a neural-net model.
  • Physics-based engine model 26 includes a core engine 28 including in serial, axial flow relationship, a low pressure compressor or booster compressor 30, a high pressure compressor 32, a combustor or burner 34, a high pressure turbine 36 and a low pressure turbine 38.
  • Core engine 28 is downstream from an inlet 40 and a fan 42.
  • Fan 42 is in serial, axial flow relationship with core engine 28 and a bypass duct 44 and a bypass nozzle 50.
  • Fan 42, compressor 30, and low pressure turbine 38 are coupled by a first shaft 52, and compressor 32 and turbine 36 are coupled with a second shaft 54.
  • a portion of airflow 58 entering inlet 40 is channeled through bypass duct 44 and is exhausted through bypass nozzle 50, and remaining airflow 58 passes through core engine 28 and is exhausted through a core engine nozzle 60.
  • Engine model 12 is known as a Component Level Model, CLM, because each component, 28, 44, 50, 42, 60, and 40 within engine model 12 is individually modeled and then assembled into a specific engine model, such as physics-based engine model 26.
  • Engine model 12 is programmed to represent a fast-running transient engine cycle that accounts for flight conditions, control variable inputs, and high-pressure compressor bleed. Further, engine model 12 includes tunable parameters such as engine component efficiencies and flows. These parameters can be modified using a parameter estimation algorithm, thereby modifying the model of a nominal or average engine to the model of a specific engine.
  • model-based trending process 10 executes 68 engine model 12 at actual trend conditions using energy and mass balance calculations and a steady-state trim process.
  • the parameter estimation (or tracking) algorithm uses actual sensor data input 20 from the engine sensors and model-computed sensor data input after nominal engine model 12 is executed 68 to estimate 70 engine component efficiencies and flow functions.
  • the parameter estimation algorithm provides component health parameter estimates in real-time, i.e., on-board engine 14 and during operation.
  • the parameter estimation algorithm is known in the art and may include, but is not limited to a linear regressor or a Kalman filter.
  • Model-based trending process 10 then adjusts or fixes 72 component efficiencies and flow functions in engine model 12 to represent the actual engine component health.
  • the component efficiencies and flow functions relate to gas turbine engine major rotating assemblies including fans, compressors, and turbines.
  • reference trend conditions e.g. takeoff operating condition
  • model-based normalization process 10 utilizes model-computed corrected sensor parameters and virtual sensors such as thrust, airflows, stall margins, and turbine inlet temperature
  • trending parameters are facilitated to be more accurately estimated using process 10 than normalized parameters obtained using known trending estimation techniques that perform simple empirical corrections to sensed parameters.
  • model-computed trend alerts such as threshold exceedences, sudden shifts, or slow drifts are facilitated to be more accurate and more representative of actual changes in engine health using process 10 than are obtainable using other known trending estimation algorithms.

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  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Mechanical Engineering (AREA)
  • Evolutionary Computation (AREA)
  • Automation & Control Theory (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Artificial Intelligence (AREA)
  • Testing And Monitoring For Control Systems (AREA)
  • Feedback Control In General (AREA)
EP01307221A 2000-11-02 2001-08-24 Verfahren und Vorrichtung zum Überwachen des Betriebes einer Gasturbine Withdrawn EP1204076A3 (de)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US09/705,131 US6466858B1 (en) 2000-11-02 2000-11-02 Methods and apparatus for monitoring gas turbine engine operation
US705131 2000-11-02

Publications (2)

Publication Number Publication Date
EP1204076A2 true EP1204076A2 (de) 2002-05-08
EP1204076A3 EP1204076A3 (de) 2005-09-14

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US (2) US6466858B1 (de)
EP (1) EP1204076A3 (de)
JP (1) JP2002180851A (de)

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